Christoph D. Mathys

This paper introduces a computational framework to measure cardiac interoceptive sensitivity by analyzing heart rate variability and finds it is weak in healthy individuals but varies significantly across participants.

The paper tests a computational model of perceived control over breathing by analyzing data from a novel behavioral task, finding that perceived control depends on interoceptive outcomes and prediction errors.

The paper uses computational modeling and fMRI to investigate how environmental uncertainty influences premature decision-making impulsivity in humans.

This study examines uncertainty processing in humans using computational models and neurochemical data, finding links between neurotransmitter levels and prediction errors in reversal learning.

The paper applies Bayesian workflow to improve parameter and model identifiability in hierarchical Bayesian models using multivariate behavioural data from a reward learning task.

This study shows that uncertainty in thermosensory expectations enhances the perception of pain in the thermal grill illusion through predictive coding mechanisms.

This study investigates how dopamine D2 and opioid receptor antagonism affects the balance between model-based and model-free behavior in healthy volunteers, finding that D2 antagonism increases model-based behavior while opioid antagonism has no significant effect.

This study shows that blocking D2/D3 dopamine receptors increases belief volatility during trust learning, particularly in individuals with higher dopamine availability.

TAPAS provides open-source tools for computational assays in psychiatry to support differential diagnosis and treatment prediction through end-to-end pipelines.

The paper investigates how people weigh self-gathered versus social information in decision-making using Bayesian models and fMRI, showing distinct brain regions for each type of information.

This study shows ketamine reduces high-level prediction errors in a roving MMN paradigm, supporting the role of NMDAR in hierarchical Bayesian inference.

The paper investigates belief updating in paranoid individuals and rats exposed to methamphetamine, finding impaired uncertainty-driven learning and rigid anticipation of environmental changes.

The paper examines abnormal processing of uncertainty in at-risk mental state individuals using computational models and fMRI, finding increased volatility estimates and altered cortical activation patterns.

This study finds that schizophrenia patients show increased choice switching in reward-based tasks, linked to heightened beliefs about environmental volatility and greater prefrontal cortex activity.

This study examines how subthalamic deep brain stimulation (STN-DBS) affects impulsivity and subjective uncertainty estimates in Parkinson’s disease patients through gambling behavior analysis.